Nava Tintarev

951 total citations
46 papers, 483 citations indexed

About

Nava Tintarev is a scholar working on Artificial Intelligence, Sociology and Political Science and Information Systems. According to data from OpenAlex, Nava Tintarev has authored 46 papers receiving a total of 483 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Artificial Intelligence, 18 papers in Sociology and Political Science and 18 papers in Information Systems. Recurrent topics in Nava Tintarev's work include Recommender Systems and Techniques (17 papers), Topic Modeling (10 papers) and Misinformation and Its Impacts (8 papers). Nava Tintarev is often cited by papers focused on Recommender Systems and Techniques (17 papers), Topic Modeling (10 papers) and Misinformation and Its Impacts (8 papers). Nava Tintarev collaborates with scholars based in Netherlands, United States and United Kingdom. Nava Tintarev's co-authors include Tim Draws, Yucheng Jin, Katrien Verbert, Oana Inel, Judith Masthoff, Ujwal Gadiraju, Derek Bridge, Mesut Kaya, Mariët Theune and Emily Sullivan and has published in prestigious journals such as Journal of Environmental Management, International Journal of Human-Computer Studies and AI Magazine.

In The Last Decade

Nava Tintarev

42 papers receiving 469 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Nava Tintarev Netherlands 15 204 174 154 65 49 46 483
Chris Newell Switzerland 4 351 1.7× 199 1.1× 143 0.9× 120 1.8× 42 0.9× 4 580
Huahai Yang United States 16 146 0.7× 397 2.3× 150 1.0× 103 1.6× 40 0.8× 31 757
Maria Soledad Pera United States 15 454 2.2× 423 2.4× 118 0.8× 59 0.9× 71 1.4× 117 805
Simon Attfield United Kingdom 13 157 0.8× 112 0.6× 109 0.7× 164 2.5× 33 0.7× 61 638
Sanjay Kairam United States 11 188 0.9× 198 1.1× 263 1.7× 114 1.8× 101 2.1× 24 690
Jennifer Trant United States 11 226 1.1× 127 0.7× 90 0.6× 121 1.9× 63 1.3× 36 607
José San Pedro Spain 8 206 1.0× 237 1.4× 152 1.0× 215 3.3× 49 1.0× 20 673
Pinata Winoto China 10 201 1.0× 114 0.7× 47 0.3× 64 1.0× 24 0.5× 45 370
Martijn Millecamp Belgium 10 111 0.5× 163 0.9× 32 0.2× 51 0.8× 73 1.5× 17 347
Bruce Ferwerda Sweden 15 140 0.7× 116 0.7× 158 1.0× 134 2.1× 52 1.1× 44 612

Countries citing papers authored by Nava Tintarev

Since Specialization
Citations

This map shows the geographic impact of Nava Tintarev's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Nava Tintarev with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nava Tintarev more than expected).

Fields of papers citing papers by Nava Tintarev

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nava Tintarev. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Nava Tintarev. The network helps show where Nava Tintarev may publish in the future.

Co-authorship network of co-authors of Nava Tintarev

This figure shows the co-authorship network connecting the top 25 collaborators of Nava Tintarev. A scholar is included among the top collaborators of Nava Tintarev based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nava Tintarev. Nava Tintarev is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Burke, Robin, Gediminas Adomavičius, Toine Bogers, et al.. (2025). De-centering the (Traditional) user: Multistakeholder evaluation of recommender systems. International Journal of Human-Computer Studies. 203. 103560–103560. 1 indexed citations
2.
Tintarev, Nava, et al.. (2025). The Pitfalls of Growing Group Complexity: LLMs and Social Choice-Based Aggregation for Group Recommendations. Research Publications (Maastricht University). 322–330.
3.
Tintarev, Nava, et al.. (2024). A Preliminary Study of the Impact of Personality on Satisfaction in Group Contexts. Research Publications (Maastricht University). 319–328. 1 indexed citations
4.
Starke, Alain D., et al.. (2024). NORMalize 2024: The Second Workshop on Normative Design and Evaluation of Recommender Systems. Research Publications (Maastricht University). 1242–1244. 1 indexed citations
5.
Tintarev, Nava, Bart P. Knijnenburg, & Martijn C. Willemsen. (2024). Measuring the benefit of increased transparency and control in news recommendation. AI Magazine. 45(2). 212–226. 1 indexed citations
6.
Draws, Tim, et al.. (2023). Evaluating explainable social choice-based aggregation strategies for group recommendation. User Modeling and User-Adapted Interaction. 34(1). 1–58. 4 indexed citations
7.
Draws, Tim, et al.. (2023). Explainable Cross-Topic Stance Detection for Search Results. Research Publications (Maastricht University). 221–235. 6 indexed citations
8.
Knijnenburg, Bart P., et al.. (2023). How do people make decisions in disclosing personal information in tourism group recommendations in competitive versus cooperative conditions?. User Modeling and User-Adapted Interaction. 34(3). 549–581. 3 indexed citations
9.
Inel, Oana, et al.. (2021). Design Implications for Explanations: A Case Study on Supporting Reflective Assessment of Potentially Misleading Videos. Frontiers in Artificial Intelligence. 4. 712072–712072.
10.
Draws, Tim, et al.. (2021). A Checklist to Combat Cognitive Biases in Crowdsourcing. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing. 9. 48–59. 54 indexed citations
11.
Delić, Amra, et al.. (2021). Factors Influencing Privacy Concern for Explanations of Group Recommendation. Research Publications (Maastricht University). 14–23. 17 indexed citations
12.
Arts, Koen, Yolanda Melero, Nirwan Sharma, et al.. (2020). On the merits and pitfalls of introducing a digital platform to aid conservation management: Volunteer data submission and the mediating role of volunteer coordinators. Journal of Environmental Management. 265. 110497–110497. 12 indexed citations
13.
Tintarev, Nava, et al.. (2018). Using Visualizations to Encourage Blind-Spot Exploration.. Conference on Recommender Systems. 53–60. 3 indexed citations
14.
Jin, Yucheng, Nava Tintarev, & Katrien Verbert. (2018). Effects of personal characteristics on music recommender systems with different levels of controllability. Lirias (KU Leuven). 13–21. 40 indexed citations
15.
Tintarev, Nava, Christoph Lofi, & Cynthia C. S. Liem. (2017). Sequences of Diverse Song Recommendations. 391–392. 8 indexed citations
16.
Smith, Kirsten A., et al.. (2015). How Can Skin Check Reminders be Personalised to Patient Conscientiousness. Bournemouth University Research Online (Bournemouth University). 4 indexed citations
17.
Tintarev, Nava, et al.. (2014). Adaptive Visualization of Plans. 2 indexed citations
18.
Smith, Kirsten A., Judith Masthoff, Nava Tintarev, & Wendy Moncur. (2014). The development and evaluation of an emotional support algorithm for carers. Discovery Research Portal (University of Dundee). 8(2). 181–196. 14 indexed citations
19.
Tintarev, Nava, et al.. (2013). SAsSy—scrutable autonomous systems. Aberdeen University Research Archive (Aberdeen University). 3 indexed citations
20.
Tintarev, Nava & Judith Masthoff. (2008). Over- and underestimation in different product domains. European Conference on Artificial Intelligence. 6 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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